Rapidly increased amounts of multimedia data require automatic classification of a wide range of semantic concepts chosen to represent multimedia content, such as objects (e.g., car), scenes (e.g., sunset), events (e.g., birthday). Multimedia data are typically collected incrementally, e.g., images and videos captured at a person's birthday party are collected throughout different years. A newly acquired multimedia collection often has different data distribution than a previously acquired multimedia collection, e.g., they come from different groups of users, have changing characteristics from time to time. To classify a concept from a newly acquired multimedia collection traditional methods, such as [S. F. Chang, et al. Large-scale multimodal semantic concept detection for consumer video, ACM MIR, pages 255-264, 2007], solely rely on data from the current collection. First, a set of labeled data are obtained in the current collection, and then a classifier, such as an SVM developed in [V. Vapnik. Statistical Learning Theory. Wiley-Interscience, New York 1998], is trained by using the labeled data to classify the remaining data in the current collection. It is, in general, very expensive to obtain a large amount of labeled data from manual annotation, and the performance of the traditional semantic concept classifier is often limited by the small amount of labeled training data. In addition, the classifier will not work well for a future new multimedia collection due to the difference between the current and future collections.